A finite element-guided physics-informed operator learning framework learns solution operators for coupled multiphysics PDEs, enabling discretization-independent predictions on arbitrary domains without labeled data.
Folax (Finite Operator Learning with JAX), a framework for solving and optimizing pdes by integrating machine learning with numerical methods in computational mechanics
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Tackling multiphysics problems via finite element-guided physics-informed operator learning
A finite element-guided physics-informed operator learning framework learns solution operators for coupled multiphysics PDEs, enabling discretization-independent predictions on arbitrary domains without labeled data.